Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
用于因果效应不确定性量化的贝叶斯目标极大似然估计AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确针对二元处理的因果效应估计及 TMLE 的贝叶斯不确定性量化，直接属于因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
用于因果效应不确定性量化的贝叶斯目标极大似然估计
- 研究问题
- 对二元处理以及二元或连续结局，如何以贝叶斯方式实现 TMLE，并量化因果效应估计中的不确定性？
- 方法与思路
- 作者提出三种方案：依次训练并使用预测均值、加入预测标准差，或通过贝叶斯网络同时训练三个模型。
- 材料陈述的贡献
- 摘要称给出三种贝叶斯实现，并在两个示例中比较经典方法，报告 BN-TMLE 在小样本中较好、大样本中相近。
- 阅读关注点
- 应核对先验和联合训练目标、不确定性向目标效应的传递、区间校准，以及经典 TMLE 的性质是否仍成立。
- 阅读边界
- 这是预印本，同行评审及接收未核实。摘要未证明贝叶斯实现保留双重稳健性，也未给区间覆盖或小样本优势数值。
原文摘要
Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum Likelihood Estimation (TMLE) algorithm, a flexible semi-parametric double robust estimator, for a probabilistic quantification of uncertainty in causal effects with binary treatment, and binary and continuous outcomes. In the first two approaches, the three TMLE models (outcome, treatment, and fluctuation) are trained sequentially. Since Bayesian implementation of treatment and outcome yields probabilistic predictions, the first approach uses mean predictions, while the second approach uses both the mean and standard deviation of predictions for training the fluctuation model (targeting step). The third approach trains all three models simultaneously through a Bayesian network (called BN-TMLE in this paper). The proposed approaches were demonstrated for two examples with binary and continuous outcomes and validated against classical implementations. This paper also investigated the effect of data sizes and model misspecifications on causal effect estimation using the BN-TMLE approach. Results showed that the proposed BN-TMLE outperformed classical implementations in small data regimes and performed similarly in large data regimes.